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The paper proposes RMS-RSP, a perturbation-sensitive method for selecting medical questions to receive rationale supervision, improving robust accuracy and semantic consistency in QA systems.
This paper introduces accuracy-blind answer churn in retrieval-augmented QA systems and proposes the Snapshot Compatibility Audit to detect hidden answer changes when the corpus is updated, even if overall accuracy appears stable.